Systems based on character recognition are widely used in both home and office functions. For example scanners, which reads printed text and translates the image being scanned to a form of data, the computer is able to process (such as ASCII code, for example). Character recognition systems include an optical camera for photographing images, and sophisticated 15 software for analyzing images. The arising need for identifying and analyzing text, characters or other signifying marks on a variety of products, vehicles and aircrafts led to the character recognition system expansion and the system being put to use in different areas of interest.
An operative system at the entrance to harbors and shipping docks, for example, requires means for identifying and monitoring trucks and container movement and placement, and automatic supply of information on any vehicle entering the area. In order for such a surveillance system to be effective, it is necessary for the system to be able to recognize and process a variety of identifying marks, such as license numbers, serial numbers and 25 various insignia.
There are growing numbers of operational ports across the world, and an increasing rate of vehicle movement through them. This factor, hand in hand with the increasing need for reinforced security precautions, clearly calls for a sophisticated character recognition system. The need for such a system that will provide information based on observations from different angles, factoring in various interfering factors such as weather conditions, lighting etc. is undeniable.
Several algorithm based systems for identifying marks or characters have been presented to date. For example a system detailed in U.S. Pat. No. 6,026,177 titled “method for identifying a sequence of alphanumeric characters”. The patent describes a set of algorithms that can be used to extract a text of numbers and characters from a digital image. It focuses on two practical applications: vision based vehicle license plate recognition (LPR) and container code recognition (CCR). The patent discloses the uses of standard image processing methods e.g. filtering, binarization, in addition to standard programming techniques such as level classification.
The main drawback of this method, and several others in the field, which limits application considerably, relates to the limited number of identifying marks recognized and processed by the method. Additionally, none of the systems in use today include a method for verification and control of the information retrieved to assess whether the identification is indeed accurate. This lack of inspection and verification greatly question the credibility of these inadequate systems.
Additionally, these systems in use today all perform the identification, adjustment, improvement and deciphering stages equally and continuously on one picture in its entirety. The process is expensive and time consuming and does not always result in a successful and reliable identification. Another major drawback when considering these systems is the need for a strong computer to run the identification programs. This is largely due to the complex algorithm employed, which is based on complicated functions and variables, which take long to run even on the strongest of computing systems. For example, some of these programs use floating points instead of integers, or rely on “heavy” algorithms, which take longer to process and compute. Thus, there is a demonstrated need for an optical character recognition method, which will enable quick and simultaneous identification of as many details as possible.                Furthermore, there is a need for a multi-functional universal system that will provide character identification in a wide variety of fields with the same success.        Additionally, the system must be able to perform self-testing and data verification to ensure reliable and repeatable data.        
Furthermore, there is a need for a system that is able to standardize and adjust levels in order to decipher a faulty image received as a result of bad visibility or severe weather conditions.
The method must also be able to run on simple computing system, which does not require expensive and complicated hardware to run smoothly and without error.
It should be able to perform an exact and verified identification in a method, which is both fast and reliable.
The method should be able to answer individual needs and demands, and offers information that is both accurate and easily and cheaply accessible.